welford.cu 54.3 KB
Newer Older
jjsjann123's avatar
jjsjann123 committed
1
2
3
4
5
6
7
8
9
10
#include <iostream>
#include <ATen/ATen.h>
#include <ATen/AccumulateType.h>
#include <ATen/cuda/CUDAContext.h>

#include <cuda.h>
#include <cuda_runtime.h>

#include <vector>

11
#include "type_shim.h"
12
#include "compat.h"
13

14
#if defined __HIP_PLATFORM_HCC__
Jeff Daily's avatar
Jeff Daily committed
15
#define SHFL_DOWN(mask,val,i) __shfl_down(val, i)
16
17
18
#else
#define SHFL_DOWN __shfl_down_sync
#endif
jjsjann123's avatar
jjsjann123 committed
19
20
21
22

__device__ __forceinline__ int lastpow2(int n)
{
  int out = 1 << (31 - __clz(n));
Jie's avatar
Jie committed
23
  if(n == out)
jjsjann123's avatar
jjsjann123 committed
24
25
26
27
28
    out >>= 1;
  return out;
}

__host__ __forceinline__ int h_next_pow2(unsigned int n) {
Marek Kolodziej's avatar
Marek Kolodziej committed
29
    n--;
jjsjann123's avatar
jjsjann123 committed
30
31
32
33
34
    n |= (n >>  1);
    n |= (n >>  2);
    n |= (n >>  4);
    n |= (n >>  8);
    n |= (n >> 16);
Marek Kolodziej's avatar
Marek Kolodziej committed
35
    return ++n;
jjsjann123's avatar
jjsjann123 committed
36
37
38
39
40
41
42
43
44
45
46
}

__host__ __forceinline__ int h_last_pow2(unsigned int n) {
    n |= (n >>  1);
    n |= (n >>  2);
    n |= (n >>  4);
    n |= (n >>  8);
    n |= (n >> 16);
    return n - (n >> 1);
}

Jeff Daily's avatar
Jeff Daily committed
47
48
49
#ifdef __HIP_PLATFORM_HCC__
#define WARP_SIZE 64
#else
jjsjann123's avatar
jjsjann123 committed
50
#define WARP_SIZE 32
Jeff Daily's avatar
Jeff Daily committed
51
#endif
jjsjann123's avatar
jjsjann123 committed
52
53
54
55
56
57

template<typename T>
__device__ __forceinline__ T warp_reduce_sum(T val)
{
  #pragma unroll
  for(int i = WARP_SIZE/2; i > 0; i >>= 1)
58
    val = val + SHFL_DOWN(0xffffffff, val, i);
jjsjann123's avatar
jjsjann123 committed
59
60
61
62
63
64
65
66
  return val;
}

template<typename T>
__device__ __forceinline__ T reduce_block(T *x, T val)
{
  int tid = threadIdx.y*blockDim.x + threadIdx.x;
  int blockSize = blockDim.x * blockDim.y;
Jeff Daily's avatar
Jeff Daily committed
67
68
  int lane = tid % WARP_SIZE;
  int wid = tid / WARP_SIZE;
jjsjann123's avatar
jjsjann123 committed
69

Jeff Daily's avatar
Jeff Daily committed
70
  if (blockSize > WARP_SIZE) {
jjsjann123's avatar
jjsjann123 committed
71
    val = warp_reduce_sum(val);
Jeff Daily's avatar
Jeff Daily committed
72
73
    if (lane == 0)
      x[wid] = val;
jjsjann123's avatar
jjsjann123 committed
74
75
76

    __syncthreads();

Jeff Daily's avatar
Jeff Daily committed
77
    val = (tid < blockSize / WARP_SIZE? x[lane] : T(0));
jjsjann123's avatar
jjsjann123 committed
78
79
  }

Jeff Daily's avatar
Jeff Daily committed
80
  if(wid==0) val = warp_reduce_sum(val);
jjsjann123's avatar
jjsjann123 committed
81
82
83
84

  return val;
}

Jie's avatar
Jie committed
85
86
#define ELEMENTS_PER_ITER 4 // enables concurrency within each thread to hide latency
#define ELEMENTS_PER_THREAD 16
Jeff Daily's avatar
Jeff Daily committed
87
#define OPTIMAL_TILE_W WARP_SIZE
Jie's avatar
Jie committed
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
#define MAX_H_BLOCK 128
#define MAX_BLOCK_SIZE 512

__host__ int div_ru(int x, int y) {
  return h_last_pow2(1 + (x-1)/y);
}

__host__ void flexible_launch_configs(
      const int reduction,
      const int stride,
      dim3 &block,
      dim3 &grid,
      const bool coop_flag = false) {
  int block_x = std::min(h_last_pow2(stride), OPTIMAL_TILE_W);
  int block_y = std::min(h_last_pow2(div_ru(reduction , ELEMENTS_PER_THREAD)),
                         MAX_BLOCK_SIZE / block_x);
  if (block_x * block_y != MAX_BLOCK_SIZE) {
    block_x = std::min(h_last_pow2(stride), MAX_BLOCK_SIZE / block_y);
  }

  int grid_x = div_ru(stride, block_x);
  int grid_y = std::min(div_ru(reduction, block_y * ELEMENTS_PER_THREAD), MAX_H_BLOCK);
  if (coop_flag) {
    // it's not worth having a grid reduction if the reduction dimension is not big enough
    grid_y = grid_y < 8 ? 1 : grid_y;
  }

  block.x = block_x;
  block.y = block_y;
  block.z = 1;
  grid.x = grid_x;
  grid.y = grid_y;
  grid.z = 1;
}

template<typename T, typename C>
__device__ __forceinline__ void welford_merge_element(C& count,
                                                      T& mean,
                                                      T& m2n,
                                                      const C& num_new,
                                                      const T& mean_new,
                                                      const T& m2n_new) {
      T factor = T(1.0) / max(1, (count + num_new));
      T delta0 = mean - mean_new;
      mean = (mean_new * num_new + mean * count) * factor;
      m2n += m2n_new + delta0 * delta0 * num_new * count * factor;
      count += num_new;
}
jjsjann123's avatar
jjsjann123 committed
136
137
138
139
140
141

template<typename T>
__device__ __forceinline__ void warp_reduce_mean_m2n(T &mean, T &m2n, int &num)
{
  #pragma unroll
  for(int i = WARP_SIZE/2; i > 0; i >>= 1) {
142
143
144
    auto num_new = SHFL_DOWN(0xffffffff, num, i);
    auto mean_new = SHFL_DOWN(0xffffffff, mean, i);
    auto m2n_new = SHFL_DOWN(0xffffffff, m2n, i);
Jie's avatar
Jie committed
145
    welford_merge_element(num, mean, m2n, num_new, mean_new, m2n_new);
jjsjann123's avatar
jjsjann123 committed
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
  }
}

template <typename T>
__device__ void welford_reduce_mean_m2n(
      T* __restrict__ x,
      int* __restrict__ count,
      T &mean,
      T &m2n,
      int &num,
      int block_size,
      int thread_id)
{
  int lane = thread_id % WARP_SIZE;
  int wid = thread_id / WARP_SIZE;

Jeff Daily's avatar
Jeff Daily committed
162
  if (block_size > WARP_SIZE) {
jjsjann123's avatar
jjsjann123 committed
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
    warp_reduce_mean_m2n(mean, m2n, num);
    if (lane == 0) {
      x[wid*2] = mean;
      x[wid*2+1] = m2n;
      count[wid] = num;
    }
    __syncthreads();

    if (wid == 0) {
      mean = (thread_id < block_size / WARP_SIZE)? x[lane*2] : T(0);
      m2n = (thread_id < block_size / WARP_SIZE)? x[lane*2+1] : T(0);
      num = (thread_id < block_size / WARP_SIZE)? count[lane] : int(0);
    }
  }

  if (wid==0) warp_reduce_mean_m2n(mean, m2n, num);

  return;
}

// return spatial size for NC+ Tensors
__host__ int get_tensor_spatial_size(const at::Tensor& input)
{
  auto space_size = input.size(2);
  for (int i = 3; i < input.ndimension(); i++) {
    space_size *= input.size(i);
  }
  return space_size;
}

// promote accumulation scalar type. promote half to float.
__host__ at::ScalarType promote_scalartype(const at::Tensor& input)
{
196
197
  return input.scalar_type() == at::ScalarType::Half ?
           at::ScalarType::Float : input.scalar_type();
jjsjann123's avatar
jjsjann123 committed
198
199
200
201
202
}

// return single element size, optional accumulation type promotion.
__host__ size_t get_element_data_size(const at::Tensor& input, bool accumulation = false)
{
203
  auto scalar_type = accumulation ? promote_scalartype(input) : input.scalar_type();
jjsjann123's avatar
jjsjann123 committed
204
205
206
  return at::elementSize(scalar_type);
}

Jie's avatar
Jie committed
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
template<typename T, typename C>
__device__ __forceinline__ void welford_merge_block_vertical(C& count,
                                                             T& mean,
                                                             T& m2n,
                                                             C* shmem_count,
                                                             T* shmem_mean,
                                                             T* shmem_m2n) {
  // write to shared memory
  auto address_base = threadIdx.x + threadIdx.y * blockDim.x;
  shmem_mean[address_base] = mean;
  shmem_m2n[address_base] = m2n;
  shmem_count[address_base] = count;

#pragma unroll
  for (int offset = blockDim.y/2; offset > 0; offset >>= 1) {
    __syncthreads();
    if (threadIdx.y < offset && threadIdx.y + offset < blockDim.y) {
      auto address = address_base + offset * blockDim.x;
      // read shared memory back to register for reduction
      auto num_new = shmem_count[address];
      auto mean_new = shmem_mean[address];
      auto m2n_new = shmem_m2n[address];

      welford_merge_element(count, mean, m2n, num_new, mean_new, m2n_new);

      // last write is not necessary
      shmem_mean[address_base] = mean;
      shmem_m2n[address_base] = m2n;
      shmem_count[address_base] = count;
    }
  }
}

template<typename T>
__device__ __forceinline__ void merge_block_vertical(T& sum_dy,
                                                     T& sum_dy_xmu,
                                                     T* shmem_sum_dy,
                                                     T* shmem_sum_dy_xmu) {
  // write to shared memory
  auto address_base = threadIdx.x + threadIdx.y * blockDim.x;
  shmem_sum_dy[address_base] = sum_dy;
  shmem_sum_dy_xmu[address_base] = sum_dy_xmu;

#pragma unroll
  for (int offset = blockDim.y/2; offset > 0; offset >>= 1) {
    __syncthreads();
    if (threadIdx.y < offset && threadIdx.y + offset < blockDim.y) {
      auto address = address_base + offset * blockDim.x;

      sum_dy += shmem_sum_dy[address];
      sum_dy_xmu += shmem_sum_dy_xmu[address];

      // last write is not necessary
      shmem_sum_dy[address_base] = sum_dy;
      shmem_sum_dy_xmu[address_base] = sum_dy_xmu;
    }
  }
}

jjsjann123's avatar
jjsjann123 committed
266
267
268

// welford kernel calculating mean/biased_variance/unbiased_variance
template <typename scalar_t, typename accscalar_t, typename outscalar_t>
Jeff Daily's avatar
Jeff Daily committed
269
270
271
#ifdef __HIP_PLATFORM_HCC__
__launch_bounds__(MAX_BLOCK_SIZE)
#endif
jjsjann123's avatar
jjsjann123 committed
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
__global__ void welford_kernel(
      const scalar_t* __restrict__ input,
      outscalar_t* __restrict__ out_mean,
      outscalar_t* __restrict__ out_var_biased,
      const int bs,
      const int fs,
      const int ss) {
  int block_size = blockDim.x * blockDim.y;
  int count = 0;
  accscalar_t x_mean = accscalar_t(0);
  accscalar_t m_2_n = accscalar_t(0);

  int thread_id = threadIdx.y*blockDim.x + threadIdx.x;

  for (int batch_id = threadIdx.y; batch_id < bs; batch_id += blockDim.y) {
    int input_base = blockIdx.x*ss + batch_id*ss*fs;
    // sequential welford
    for (int offset = threadIdx.x; offset < ss ; offset += blockDim.x) {
      count++;
      auto x_n = static_cast<accscalar_t>(input[offset+input_base]);
Jie's avatar
Jie committed
292
293
294
      auto d = x_n - x_mean;
      x_mean += d / count;
      m_2_n += d * (x_n - x_mean);
jjsjann123's avatar
jjsjann123 committed
295
296
297
    }
  }

Jeff Daily's avatar
Jeff Daily committed
298
299
  static __shared__ int s_mem[WARP_SIZE];
  static __shared__ accscalar_t s_mem_ac[WARP_SIZE*2];
Jie's avatar
Jie committed
300

jjsjann123's avatar
jjsjann123 committed
301
302
303
304
305
306
307
308
309
310
  welford_reduce_mean_m2n<accscalar_t>(s_mem_ac, s_mem, x_mean, m_2_n, count, block_size, thread_id);

  if (thread_id == 0) {
    out_mean[blockIdx.x] = static_cast<outscalar_t>(x_mean);
    out_var_biased[blockIdx.x] = static_cast<outscalar_t>(m_2_n/count);
  }
}

// elementwise BN kernel
template <typename scalar_t, typename accscalar_t, typename layerscalar_t>
Jeff Daily's avatar
Jeff Daily committed
311
312
313
#ifdef __HIP_PLATFORM_HCC__
__launch_bounds__(MAX_BLOCK_SIZE)
#endif
jjsjann123's avatar
jjsjann123 committed
314
315
316
__global__ void batchnorm_forward_kernel(
      const scalar_t* __restrict__ input,
      const accscalar_t* __restrict__ mean,
Jie's avatar
Jie committed
317
      const accscalar_t* __restrict__ inv_std,
jjsjann123's avatar
jjsjann123 committed
318
319
320
321
      const layerscalar_t* __restrict__ weight,
      const layerscalar_t* __restrict__ shift,
      scalar_t* __restrict__ out,
      const int ss,
Jie's avatar
Jie committed
322
      const int bs) {
jjsjann123's avatar
jjsjann123 committed
323
  auto m_c = mean[blockIdx.x];
Jie's avatar
Jie committed
324
  auto inv_std_c = inv_std[blockIdx.x];
325
326
  auto w_c = weight == NULL ? accscalar_t(1.0) : static_cast<accscalar_t>(weight[blockIdx.x]);
  auto s_c = shift == NULL ? accscalar_t(0.0) : static_cast<accscalar_t>(shift[blockIdx.x]);
jjsjann123's avatar
jjsjann123 committed
327

Jie's avatar
Jie committed
328
329
330
331
332
  for (int batch_offset = blockIdx.y*blockDim.y + threadIdx.y; batch_offset < bs; batch_offset += gridDim.y*blockDim.y) {
    int address_base = blockIdx.x*ss + batch_offset*gridDim.x*ss;
    for (int offset = threadIdx.x + blockIdx.z*blockDim.x; offset < ss ; offset+= gridDim.z*blockDim.x) {
      out[address_base+offset] = static_cast<scalar_t>(w_c * (static_cast<accscalar_t>(input[address_base+offset]) - m_c ) * inv_std_c + s_c);
    }
jjsjann123's avatar
jjsjann123 committed
333
334
335
336
337
338
339
340
  }
}

// Backward BN kernel, calculates grad_bias, grad_weight as well as intermediate
// results to calculating grad_input.
// Breaking the grad_input to two step to support sync BN, which requires all
// reduce of the intermediate results across processes.
template <typename scalar_t, typename accscalar_t, typename layerscalar_t>
Jeff Daily's avatar
Jeff Daily committed
341
342
343
#ifdef __HIP_PLATFORM_HCC__
__launch_bounds__(MAX_BLOCK_SIZE)
#endif
jjsjann123's avatar
jjsjann123 committed
344
345
346
347
__global__ void reduce_bn_kernel(
      const scalar_t* __restrict__ input,
      const scalar_t* __restrict__ grad_output,
      const accscalar_t* __restrict__ mean,
Jie's avatar
Jie committed
348
      const accscalar_t* __restrict__ inv_std,
jjsjann123's avatar
jjsjann123 committed
349
350
      accscalar_t* __restrict__ sum_dy_o,
      accscalar_t* __restrict__ sum_dy_xmu_o,
jjsjann123's avatar
jjsjann123 committed
351
352
353
354
      layerscalar_t* __restrict__ grad_weight,
      layerscalar_t* __restrict__ grad_bias,
      const int bs,
      const int fs,
Jie's avatar
Jie committed
355
      const int ss) {
Jeff Daily's avatar
Jeff Daily committed
356
  static __shared__ int s_mem[WARP_SIZE];
jjsjann123's avatar
jjsjann123 committed
357
  //int total_item_num = bs * ss;
jjsjann123's avatar
jjsjann123 committed
358
359
360
361

  int thread_id = threadIdx.y*blockDim.x + threadIdx.x;

  auto r_mean = mean[blockIdx.x];
Jie's avatar
Jie committed
362
  auto factor = inv_std[blockIdx.x];
jjsjann123's avatar
jjsjann123 committed
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390

  // Kahan sum
  accscalar_t sum_dy = 0.0;
  accscalar_t sum_dy_xmu = 0.0;
  accscalar_t sum_dy_c = 0.0;
  accscalar_t sum_dy_xmu_c = 0.0;
  for (int batch_id = threadIdx.y; batch_id < bs; batch_id += blockDim.y) {
    int input_base = blockIdx.x*ss + batch_id*ss*fs;
    for (int offset = threadIdx.x; offset < ss ; offset += blockDim.x) {
      auto e_grad = static_cast<accscalar_t>(grad_output[offset+input_base]);
      auto e_input = static_cast<accscalar_t>(input[offset+input_base]);
      // calculating sum_dy
      auto sum_dy_y = e_grad - sum_dy_c;
      auto sum_dy_t = sum_dy + sum_dy_y;
      sum_dy_c = (sum_dy_t - sum_dy) - sum_dy_y;
      sum_dy = sum_dy_t;

      // calculating sum_dy_xmu
      auto sum_dy_xmu_y = e_grad * (e_input - r_mean) - sum_dy_xmu_c;
      auto sum_dy_xmu_t = sum_dy_xmu + sum_dy_xmu_y;
      sum_dy_xmu_c = (sum_dy_xmu_t - sum_dy_xmu) - sum_dy_xmu_y;
      sum_dy_xmu = sum_dy_xmu_t;
    }
  }

  sum_dy = reduce_block((accscalar_t*)s_mem, sum_dy);
  __syncthreads();
  sum_dy_xmu = reduce_block((accscalar_t*)s_mem, sum_dy_xmu);
Jie's avatar
Jie committed
391

jjsjann123's avatar
jjsjann123 committed
392
  if (thread_id == 0) {
393
394
395
396
397
398
    if (grad_bias != NULL) {
      grad_bias[blockIdx.x] = static_cast<layerscalar_t>(sum_dy);
    }
    if (grad_weight != NULL) {
      grad_weight[blockIdx.x] = static_cast<layerscalar_t>(sum_dy_xmu * factor);
    }
jjsjann123's avatar
jjsjann123 committed
399
400
401
402
    //mean_dy[blockIdx.x] = sum_dy / total_item_num;
    //mean_dy_xmu[blockIdx.x] = sum_dy_xmu / total_item_num;
    sum_dy_o[blockIdx.x] = sum_dy;
    sum_dy_xmu_o[blockIdx.x] = sum_dy_xmu;
jjsjann123's avatar
jjsjann123 committed
403
404
405
406
407
  }
}

// elementwise backward BN kernel
template <typename scalar_t, typename accscalar_t, typename layerscalar_t>
Jeff Daily's avatar
Jeff Daily committed
408
409
410
#ifdef __HIP_PLATFORM_HCC__
__launch_bounds__(MAX_BLOCK_SIZE)
#endif
jjsjann123's avatar
jjsjann123 committed
411
412
413
414
__global__ void batchnorm_backward_kernel(
      const scalar_t* __restrict__ grad_output,
      const scalar_t* __restrict__ input,
      const accscalar_t* __restrict__ mean,
Jie's avatar
Jie committed
415
      const accscalar_t* __restrict__ inv_std,
jjsjann123's avatar
jjsjann123 committed
416
      const layerscalar_t* __restrict__ weight,
jjsjann123's avatar
jjsjann123 committed
417
418
419
      const accscalar_t* __restrict__ sum_dy,
      const accscalar_t* __restrict__ sum_dy_xmu,
      const int* __restrict__ numel,
jjsjann123's avatar
jjsjann123 committed
420
      scalar_t* __restrict__ grad_input,
jjsjann123's avatar
jjsjann123 committed
421
      const int64_t world_size,
jjsjann123's avatar
jjsjann123 committed
422
      const int ss,
Jie's avatar
Jie committed
423
      const int bs) {
jjsjann123's avatar
jjsjann123 committed
424
425
426
427
  int64_t div = 0;
  for (int i = 0; i < world_size; i++) {
    div += numel[i];
  }
jjsjann123's avatar
jjsjann123 committed
428
  auto m_c = static_cast<accscalar_t>(mean[blockIdx.x]);
jjsjann123's avatar
jjsjann123 committed
429
430
  //auto m_dy_c = static_cast<accscalar_t>(mean_dy[blockIdx.x]);
  auto m_dy_c = static_cast<accscalar_t>(sum_dy[blockIdx.x]) / div;
Jie's avatar
Jie committed
431
  auto factor_1_c = inv_std[blockIdx.x];
432
  auto factor_2_c = (weight == NULL ? accscalar_t(1.0) : static_cast<accscalar_t>(weight[blockIdx.x])) * factor_1_c;
jjsjann123's avatar
jjsjann123 committed
433
434
  //factor_1_c = factor_1_c * factor_1_c * mean_dy_xmu[blockIdx.x];
  factor_1_c = factor_1_c * factor_1_c * sum_dy_xmu[blockIdx.x] / div;
jjsjann123's avatar
jjsjann123 committed
435

Jie's avatar
Jie committed
436
437
438
  for (int batch_offset = blockIdx.y*blockDim.y+threadIdx.y; batch_offset < bs; batch_offset += gridDim.y*blockDim.y) {
    int address_base = blockIdx.x*ss + batch_offset*gridDim.x*ss;
    for (int offset = threadIdx.x + blockIdx.z*blockDim.x; offset < ss ; offset+= gridDim.z*blockDim.x) {
Jie's avatar
Jie committed
439
      grad_input[address_base+offset] = (static_cast<accscalar_t>(grad_output[address_base+offset]) - m_dy_c - (static_cast<accscalar_t>(input[address_base+offset]) - m_c) * factor_1_c) * factor_2_c;
Jie's avatar
Jie committed
440
    }
jjsjann123's avatar
jjsjann123 committed
441
442
443
  }
}

Jie's avatar
Jie committed
444
445
446
447
448
449
// welford kernel for c last tensor calculating mean/biased_variance/unbiased_variance
template
   <typename scalar_t,
    typename accscalar_t,
    typename outscalar_t,
    int PARALLEL_LOADS>
Jeff Daily's avatar
Jeff Daily committed
450
451
452
#ifdef __HIP_PLATFORM_HCC__
__launch_bounds__(MAX_BLOCK_SIZE)
#endif
Jie's avatar
Jie committed
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
__global__ void
welford_kernel_c_last(
      const scalar_t* __restrict__ input,
      outscalar_t* __restrict__ out_mean,
      outscalar_t* __restrict__ out_var_biased,
      volatile accscalar_t* staging_data,
      int* semaphores,
      const int reduction_size,
      const int stride) {
  // hide latency with concurrency
  accscalar_t x_mean[PARALLEL_LOADS];
  accscalar_t m_2_n[PARALLEL_LOADS];
  int count[PARALLEL_LOADS];

#pragma unroll
  for (int i = 0; i < PARALLEL_LOADS; i++) {
    x_mean[i] = accscalar_t(0);
    m_2_n[i] = accscalar_t(0);
    count[i] = accscalar_t(0);
  }
  // tensor dimension (m,c)

  // loop along m dimension
  int inner_loop_stride = blockDim.y * gridDim.y;

  // offset along m dimension
  int m_offset = blockIdx.y * blockDim.y + threadIdx.y;
  int c_offset = blockIdx.x * blockDim.x + threadIdx.x;

  int loop_count = 1 + (reduction_size - 1) / (inner_loop_stride * PARALLEL_LOADS);
  int address_base = m_offset * stride + c_offset;
  int address_increment = inner_loop_stride * stride;

  for (int i = 0; i < loop_count; i++) {
    accscalar_t x_math[PARALLEL_LOADS];
    accscalar_t x_count_inv[PARALLEL_LOADS];
    accscalar_t is_valid[PARALLEL_LOADS];

    // load multiple data in
#pragma unroll
    for (int j = 0; j < PARALLEL_LOADS; j++) {
      if (c_offset < stride && m_offset < reduction_size) {
        x_math[j] = input[address_base];
        count[j]++;
        x_count_inv[j] = accscalar_t(1) / count[j];
        is_valid[j] = accscalar_t(1);
      } else {
        x_math[j] = accscalar_t(0);
        x_count_inv[j] = accscalar_t(0);
        is_valid[j] = accscalar_t(0);
      }
      m_offset += inner_loop_stride;
      address_base += address_increment;
    }

    // calculate mean/m2n with welford
#pragma unroll
    for (int j = 0; j < PARALLEL_LOADS; j++) {
      accscalar_t delta0 = x_math[j] - x_mean[j];
      x_mean[j] += delta0 * x_count_inv[j];
      accscalar_t delta1 = x_math[j] - x_mean[j];
      m_2_n[j] += delta0 * delta1 * is_valid[j];
    }
  }

  // thread reduction to accumulate mean/m_2_n/count between PARALLEL_LOADS
#pragma unroll
  for (int j = 1; j < PARALLEL_LOADS; j++) {
    welford_merge_element(count[0], x_mean[0], m_2_n[0], count[j], x_mean[j], m_2_n[j]);
  }

  // release x_mean / m_2_n
  auto mean_th = x_mean[0];
  auto m2_th = m_2_n[0];
  auto count_th = count[0];

  // block-wise reduction with shared memory (since reduction cannot be done within a warp)
  static __shared__ accscalar_t shmem_mean[MAX_BLOCK_SIZE];
  static __shared__ accscalar_t shmem_m2n[MAX_BLOCK_SIZE];
  static __shared__ int shmem_count[MAX_BLOCK_SIZE];

  welford_merge_block_vertical(count_th, mean_th, m2_th, shmem_count, shmem_mean, shmem_m2n);

  // grid reduction if needed (coop launch used at the first place)
  if (gridDim.y > 1) {
    volatile accscalar_t* staging_mean = staging_data;
    volatile accscalar_t* staging_m2n = &staging_data[stride*gridDim.y];
    volatile int* staging_count = reinterpret_cast<volatile int*>(&staging_m2n[stride*gridDim.y]);

    address_base = c_offset + blockIdx.y * stride;
    // write data to staging_data;
    if (threadIdx.y == 0 && c_offset < stride) {
      staging_mean[address_base] = mean_th;
      staging_m2n[address_base] = m2_th;
      staging_count[address_base] = count_th;
    }

    __threadfence();
Jie's avatar
Jie committed
551
    __syncthreads(); // ensuring writes to staging_ is visible to all blocks
Jie's avatar
Jie committed
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593

    __shared__ bool is_last_block_done;
    // mark block done
    if (threadIdx.x == 0 && threadIdx.y == 0) {
      int old = atomicAdd(&semaphores[blockIdx.x], 1);
      is_last_block_done = (old == (gridDim.y-1));
    }

    __syncthreads();

    // check that all data is now available in global memory
    if (is_last_block_done) {
      count_th = 0;
      mean_th = accscalar_t(0.0);
      m2_th = accscalar_t(0.0);

      for (int y = threadIdx.y; y < gridDim.y; y += blockDim.y) {
        address_base = c_offset + y * stride;
        int num_new = c_offset < stride ? staging_count[address_base] : 0;
        accscalar_t mean_new = c_offset < stride ? staging_mean[address_base] : accscalar_t(0.0);
        accscalar_t m2n_new = c_offset < stride ? staging_m2n[address_base] : accscalar_t(0.0);

        welford_merge_element(count_th, mean_th, m2_th, num_new, mean_new, m2n_new);
      }

      welford_merge_block_vertical(count_th, mean_th, m2_th, shmem_count, shmem_mean, shmem_m2n);
      if (threadIdx.y == 0 && c_offset < stride) {
        out_mean[c_offset] = static_cast<outscalar_t>(mean_th);
        out_var_biased[c_offset] = static_cast<outscalar_t>(m2_th / count_th);
      }
    }
  } else {
    if (blockIdx.y == 0 && threadIdx.y == 0 && c_offset < stride) {
      out_mean[c_offset] = static_cast<outscalar_t>(mean_th);
      out_var_biased[c_offset] = static_cast<outscalar_t>(m2_th / count_th);
    }
  }
}

// parallel welford kernel to further reduce mean / biased_var
// into mean / unbiased_var / inv_std across multiple processes.
template <typename scalar_t>
Jeff Daily's avatar
Jeff Daily committed
594
595
596
#ifdef __HIP_PLATFORM_HCC__
__launch_bounds__(MAX_BLOCK_SIZE)
#endif
jjsjann123's avatar
jjsjann123 committed
597
598
599
__global__ void welford_kernel_parallel(
      const scalar_t* __restrict__ mean,
      const scalar_t* __restrict__ var_biased,
jjsjann123's avatar
jjsjann123 committed
600
      const int* __restrict__ numel,
jjsjann123's avatar
jjsjann123 committed
601
602
      scalar_t* __restrict__ out_mean,
      scalar_t* __restrict__ out_var,
Jie's avatar
Jie committed
603
604
605
      scalar_t* __restrict__ inv_std,
      const int world_size,
      const int feature_size,
jjsjann123's avatar
jjsjann123 committed
606
      const float eps) {
jjsjann123's avatar
jjsjann123 committed
607

Jie's avatar
Jie committed
608
609
610
611
612
613
614
  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < feature_size; i += gridDim.x * blockDim.x) {
    // load data;
    int address = i;
    scalar_t x_mean = 0;
    scalar_t m_2_n = 0;
    int count = 0;
    for (int j = 0; j < world_size; j++) {
jjsjann123's avatar
jjsjann123 committed
615
      welford_merge_element(count, x_mean, m_2_n, numel[j], mean[address], var_biased[address]*numel[j]);
Jie's avatar
Jie committed
616
617
618
619
620
621
622
      address += feature_size;
    }
    out_mean[i] = x_mean;
    out_var[i] = m_2_n/ (count - 1);
    inv_std[i] = scalar_t(1) / sqrt(m_2_n/count + eps);
  }
}
jjsjann123's avatar
jjsjann123 committed
623

Jie's avatar
Jie committed
624
625
626
627
628
629
// elementwise BN kernel
template <
    typename scalar_t,
    typename accscalar_t,
    typename layerscalar_t,
    int PARALLEL_LOADS>
Jeff Daily's avatar
Jeff Daily committed
630
631
632
#ifdef __HIP_PLATFORM_HCC__
__launch_bounds__(MAX_BLOCK_SIZE)
#endif
Jie's avatar
Jie committed
633
634
__global__ void batchnorm_forward_c_last_kernel(
      const scalar_t* __restrict__ input,
jjsjann123's avatar
jjsjann123 committed
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
      const scalar_t* __restrict__ z,
      const accscalar_t* __restrict__ mean,
      const accscalar_t* __restrict__ inv_std,
      const layerscalar_t* __restrict__ weight,
      const layerscalar_t* __restrict__ shift,
      scalar_t* __restrict__ out,
      const int reduction_size,
      const int stride,
      const bool fuse_relu) {
  // tensor dimension (m,c)
  // loop along m dimension
  int inner_loop_stride = blockDim.y * gridDim.y;

  // offset along m dimension
  int m_offset = blockIdx.y * blockDim.y + threadIdx.y;
  int c_offset = blockIdx.x * blockDim.x + threadIdx.x;

  auto m_c = mean[c_offset];
  auto inv_std_c = static_cast<accscalar_t>(inv_std[c_offset]);
  auto w_c = weight == NULL ? accscalar_t(1.0) : static_cast<accscalar_t>(weight[c_offset]);
  auto s_c = shift == NULL ? accscalar_t(0.0) : static_cast<accscalar_t>(shift[c_offset]);

  int loop_count = 1 + (reduction_size - 1) / (inner_loop_stride * PARALLEL_LOADS);
  int address_base = m_offset * stride + c_offset;
  int address_increment = inner_loop_stride * stride;

  for (int i = 0; i < loop_count; i++) {
#pragma unroll
    for (int j = 0; j < PARALLEL_LOADS; j++) {
      if (c_offset < stride && m_offset < reduction_size) {
        auto tmp = w_c * (static_cast<accscalar_t>(input[address_base]) - m_c ) * inv_std_c + s_c;
        if (z != NULL) {
          tmp += z[address_base];
        }
        out[address_base] = (fuse_relu && tmp <= accscalar_t(0.0) ? scalar_t(0.0) : static_cast<scalar_t>(tmp));
      }
      m_offset += inner_loop_stride;
      address_base += address_increment;
    }
  }
}

// elementwise BN kernel
template <
    typename scalar_t,
    typename accscalar_t,
    typename layerscalar_t,
    int PARALLEL_LOADS>
Jeff Daily's avatar
Jeff Daily committed
683
684
685
#ifdef __HIP_PLATFORM_HCC__
__launch_bounds__(MAX_BLOCK_SIZE)
#endif
jjsjann123's avatar
jjsjann123 committed
686
687
688
689
__global__ void relu_backward_c_last_kernel(
      const scalar_t* __restrict__ grad_output,
      const scalar_t* __restrict__ input,
      const scalar_t* __restrict__ z,
Jie's avatar
Jie committed
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
      const accscalar_t* __restrict__ mean,
      const accscalar_t* __restrict__ inv_std,
      const layerscalar_t* __restrict__ weight,
      const layerscalar_t* __restrict__ shift,
      scalar_t* __restrict__ out,
      const int reduction_size,
      const int stride) {
  // tensor dimension (m,c)
  // loop along m dimension
  int inner_loop_stride = blockDim.y * gridDim.y;

  // offset along m dimension
  int m_offset = blockIdx.y * blockDim.y + threadIdx.y;
  int c_offset = blockIdx.x * blockDim.x + threadIdx.x;

  auto m_c = mean[c_offset];
  auto inv_std_c = static_cast<accscalar_t>(inv_std[c_offset]);
707
708
  auto w_c = weight == NULL ? accscalar_t(1.0) : static_cast<accscalar_t>(weight[c_offset]);
  auto s_c = shift == NULL ? accscalar_t(0.0) : static_cast<accscalar_t>(shift[c_offset]);
Jie's avatar
Jie committed
709
710
711
712
713
714
715
716
717

  int loop_count = 1 + (reduction_size - 1) / (inner_loop_stride * PARALLEL_LOADS);
  int address_base = m_offset * stride + c_offset;
  int address_increment = inner_loop_stride * stride;

  for (int i = 0; i < loop_count; i++) {
#pragma unroll
    for (int j = 0; j < PARALLEL_LOADS; j++) {
      if (c_offset < stride && m_offset < reduction_size) {
jjsjann123's avatar
jjsjann123 committed
718
719
720
721
722
        auto tmp = w_c * (static_cast<accscalar_t>(input[address_base]) - m_c ) * inv_std_c + s_c;
        if (z != NULL) {
          tmp += z[address_base];
        }
        out[address_base] = (tmp <= accscalar_t(0.0) ? scalar_t(0.0) : grad_output[address_base]);
Jie's avatar
Jie committed
723
724
725
726
727
728
      }
      m_offset += inner_loop_stride;
      address_base += address_increment;
    }
  }
}
jjsjann123's avatar
jjsjann123 committed
729

Jie's avatar
Jie committed
730
731
732
733
734
735
// batchnorm backward kernel for c last tensor
template
   <typename scalar_t,
    typename accscalar_t,
    typename layerscalar_t,
    int PARALLEL_LOADS>
Jeff Daily's avatar
Jeff Daily committed
736
737
738
#ifdef __HIP_PLATFORM_HCC__
__launch_bounds__(MAX_BLOCK_SIZE)
#endif
Jie's avatar
Jie committed
739
740
741
742
743
__global__ void reduce_bn_c_last_kernel(
      const scalar_t* __restrict__ input,
      const scalar_t* __restrict__ grad_output,
      const accscalar_t* __restrict__ mean,
      const accscalar_t* __restrict__ inv_std,
jjsjann123's avatar
jjsjann123 committed
744
745
      accscalar_t* __restrict__ sum_dy_o,
      accscalar_t* __restrict__ sum_dy_xmu_o,
Jie's avatar
Jie committed
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
      layerscalar_t* __restrict__ grad_weight,
      layerscalar_t* __restrict__ grad_bias,
      volatile accscalar_t* staging_data,
      int* semaphores,
      const int reduction_size,
      const int stride) {

  // hide latency with concurrency
  accscalar_t sum_dy[PARALLEL_LOADS];
  accscalar_t sum_dy_xmu[PARALLEL_LOADS];

#pragma unroll
  for (int i = 0; i < PARALLEL_LOADS; i++) {
    sum_dy[i] = accscalar_t(0);
    sum_dy_xmu[i] = accscalar_t(0);
  }
  // tensor dimension (m,c)

  // loop along m dimension
  int inner_loop_stride = blockDim.y * gridDim.y;

  // offset along m dimension
  int m_offset = blockIdx.y * blockDim.y + threadIdx.y;
  int c_offset = blockIdx.x * blockDim.x + threadIdx.x;

  int loop_count = 1 + (reduction_size - 1) / (inner_loop_stride * PARALLEL_LOADS);
  int address_base = m_offset * stride + c_offset;
  int address_increment = inner_loop_stride * stride;

  auto r_mean = mean[c_offset];
  auto factor = inv_std[c_offset];

  for (int i = 0; i < loop_count; i++) {
    accscalar_t x_input[PARALLEL_LOADS];
    accscalar_t x_grad_output[PARALLEL_LOADS];

    // load multiple data in
#pragma unroll
    for (int j = 0; j < PARALLEL_LOADS; j++) {
      if (c_offset < stride && m_offset < reduction_size) {
        x_input[j] = input[address_base];
        x_grad_output[j] = grad_output[address_base];
      } else {
        x_input[j] = accscalar_t(0);
        x_grad_output[j] = accscalar_t(0);
      }
      m_offset += inner_loop_stride;
      address_base += address_increment;
    }
jjsjann123's avatar
jjsjann123 committed
795

Jie's avatar
Jie committed
796
797
798
799
800
801
802
    // calculate sum_dy / sum_dy_xmu
#pragma unroll
    for (int j = 0; j < PARALLEL_LOADS; j++) {
      sum_dy[j] += x_grad_output[j];
      sum_dy_xmu[j] += x_grad_output[j] * (x_input[j] - r_mean);
    }
  }
jjsjann123's avatar
jjsjann123 committed
803

Jie's avatar
Jie committed
804
805
806
807
808
809
  // thread reduction to accumulate sum_dy / sum_dy_xmu between PARALLEL_LOADS
#pragma unroll
  for (int j = 1; j < PARALLEL_LOADS; j++) {
    sum_dy[0] += sum_dy[j];
    sum_dy_xmu[0] += sum_dy_xmu[j];
  }
jjsjann123's avatar
jjsjann123 committed
810

Jie's avatar
Jie committed
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
  // release array of registers
  auto sum_dy_th = sum_dy[0];
  auto sum_dy_xmu_th = sum_dy_xmu[0];

  // block-wise reduction with shared memory (since reduction cannot be done within a warp)
  static __shared__ accscalar_t shmem_sum_dy[MAX_BLOCK_SIZE];
  static __shared__ accscalar_t shmem_sum_dy_xmu[MAX_BLOCK_SIZE];

  merge_block_vertical(sum_dy_th, sum_dy_xmu_th, shmem_sum_dy, shmem_sum_dy_xmu);

  // grid reduction if needed (coop launch used at the first place)
  if (gridDim.y > 1) {
    volatile accscalar_t* staging_sum_dy = staging_data;
    volatile accscalar_t* staging_sum_dy_xmu = &staging_data[stride*gridDim.y];

    address_base = c_offset + blockIdx.y * stride;
    // write data to staging_data;
    if (threadIdx.y == 0 && c_offset < stride) {
      staging_sum_dy[address_base] = sum_dy_th;
      staging_sum_dy_xmu[address_base] = sum_dy_xmu_th;
    }

    __threadfence();
Jie's avatar
Jie committed
834
    __syncthreads(); // ensuring writes to staging_ is visible to all blocks
Jie's avatar
Jie committed
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857

    __shared__ bool is_last_block_done;
    // mark block done
    if (threadIdx.x == 0 && threadIdx.y == 0) {
      int old = atomicAdd(&semaphores[blockIdx.x], 1);
      is_last_block_done = (old == (gridDim.y-1));
    }

    __syncthreads();

    // check that all data is now available in global memory
    if (is_last_block_done) {
      sum_dy_th = accscalar_t(0.0);
      sum_dy_xmu_th = accscalar_t(0.0);

      for (int y = threadIdx.y; y < gridDim.y; y += blockDim.y) {
        address_base = c_offset + y * stride;
        sum_dy_th += (c_offset < stride ? staging_sum_dy[address_base] : accscalar_t(0.0));
        sum_dy_xmu_th += (c_offset < stride ? staging_sum_dy_xmu[address_base] : accscalar_t(0.0));
      }

      merge_block_vertical(sum_dy_th, sum_dy_xmu_th, shmem_sum_dy, shmem_sum_dy_xmu);
      if (threadIdx.y == 0 && c_offset < stride) {
858
859
860
861
862
863
        if (grad_bias != NULL) {
          grad_bias[c_offset] = static_cast<layerscalar_t>(sum_dy_th);
        }
        if (grad_weight != NULL) {
          grad_weight[c_offset] = static_cast<layerscalar_t>(sum_dy_xmu_th * factor);
        }
jjsjann123's avatar
jjsjann123 committed
864
865
866
867
        //mean_dy[c_offset] = sum_dy_th / reduction_size;
        //mean_dy_xmu[c_offset] = sum_dy_xmu_th / reduction_size;
        sum_dy_o[c_offset] = sum_dy_th;
        sum_dy_xmu_o[c_offset] = sum_dy_xmu_th;
Jie's avatar
Jie committed
868
869
870
871
      }
    }
  } else {
    if (blockIdx.y == 0 && threadIdx.y == 0 && c_offset < stride) {
872
873
874
875
876
877
      if (grad_bias != NULL) {
        grad_bias[c_offset] = static_cast<layerscalar_t>(sum_dy_th);
      }
      if (grad_weight != NULL) {
        grad_weight[c_offset] = static_cast<layerscalar_t>(sum_dy_xmu_th * factor);
      }
jjsjann123's avatar
jjsjann123 committed
878
879
880
881
      //mean_dy[c_offset] = sum_dy_th / reduction_size;
      //mean_dy_xmu[c_offset] = sum_dy_xmu_th / reduction_size;
      sum_dy_o[c_offset] = sum_dy_th;
      sum_dy_xmu_o[c_offset] = sum_dy_xmu_th;
Jie's avatar
Jie committed
882
    }
jjsjann123's avatar
jjsjann123 committed
883
884
  }
}
Jie's avatar
Jie committed
885

Jie's avatar
Jie committed
886
887
888
889
890
891
// elementwise BN kernel
template <
    typename scalar_t,
    typename accscalar_t,
    typename layerscalar_t,
    int PARALLEL_LOADS>
Jeff Daily's avatar
Jeff Daily committed
892
893
894
#ifdef __HIP_PLATFORM_HCC__
__launch_bounds__(MAX_BLOCK_SIZE)
#endif
Jie's avatar
Jie committed
895
896
897
898
899
900
__global__ void batchnorm_backward_c_last_kernel(
      const scalar_t* __restrict__ grad_output,
      const scalar_t* __restrict__ input,
      const accscalar_t* __restrict__ mean,
      const accscalar_t* __restrict__ inv_std,
      const layerscalar_t* __restrict__ weight,
jjsjann123's avatar
jjsjann123 committed
901
902
903
      const accscalar_t* __restrict__ sum_dy,
      const accscalar_t* __restrict__ sum_dy_xmu,
      const int* __restrict__ numel,
Jie's avatar
Jie committed
904
      scalar_t* __restrict__ grad_input,
jjsjann123's avatar
jjsjann123 committed
905
      const int64_t world_size,
Jie's avatar
Jie committed
906
907
      const int reduction_size,
      const int stride) {
jjsjann123's avatar
jjsjann123 committed
908
909
910
911
  int64_t div = 0;
  for (int i = 0; i < world_size; i++) {
    div += numel[i];
  }
Jie's avatar
Jie committed
912
913
914
915
916
917
918
919
920
  // tensor dimension (m,c)
  // loop along m dimension
  int inner_loop_stride = blockDim.y * gridDim.y;

  // offset along m dimension
  int m_offset = blockIdx.y * blockDim.y + threadIdx.y;
  int c_offset = blockIdx.x * blockDim.x + threadIdx.x;

  auto m_c = mean[c_offset];
jjsjann123's avatar
jjsjann123 committed
921
  auto m_dy_c = sum_dy[c_offset] / div;
Jie's avatar
Jie committed
922
  auto factor_1_c = inv_std[c_offset];
923
  auto factor_2_c = (weight == NULL? accscalar_t(1.0) : static_cast<accscalar_t>(weight[c_offset])) * factor_1_c;
jjsjann123's avatar
jjsjann123 committed
924
  factor_1_c = factor_1_c * factor_1_c * sum_dy_xmu[c_offset] / div;
Jie's avatar
Jie committed
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943

  int loop_count = 1 + (reduction_size - 1) / (inner_loop_stride * PARALLEL_LOADS);
  int address_base = m_offset * stride + c_offset;
  int address_increment = inner_loop_stride * stride;

  for (int i = 0; i < loop_count; i++) {
#pragma unroll
    for (int j = 0; j < PARALLEL_LOADS; j++) {
      if (c_offset < stride && m_offset < reduction_size) {
        grad_input[address_base] = static_cast<scalar_t>(
            (static_cast<accscalar_t>(grad_output[address_base]) - m_dy_c -
            (static_cast<accscalar_t>(input[address_base]) - m_c) * factor_1_c)
            * factor_2_c);
      }
      m_offset += inner_loop_stride;
      address_base += address_increment;
    }
  }
}
jjsjann123's avatar
jjsjann123 committed
944
945
946
947
948
949
950
951
952
953
954

std::vector<at::Tensor> welford_mean_var_CUDA(const at::Tensor input) {
  const auto batch_size = input.size(0);
  const auto feature_size = input.size(1);

  auto space_size = get_tensor_spatial_size(input);
  auto scalar_type = promote_scalartype(input);

  at::Tensor out_var_biased = at::empty({feature_size}, input.options().dtype(scalar_type));
  at::Tensor out_mean = at::empty({feature_size}, input.options().dtype(scalar_type));

Jeff Daily's avatar
Jeff Daily committed
955
  int block_y = min(h_last_pow2(batch_size), int(MAX_BLOCK_SIZE / WARP_SIZE));
Jie's avatar
Jie committed
956
  int block_x = max(1, min(MAX_BLOCK_SIZE / block_y, h_last_pow2(space_size)));
jjsjann123's avatar
jjsjann123 committed
957
958
959
960
961
  const dim3 block(block_x, block_y);
  const dim3 grid(feature_size);

  auto stream = at::cuda::getCurrentCUDAStream();

962
963
  {
    using namespace at;
964
965
966
    DISPATCH_FLOAT_AND_HALF(input.scalar_type(), 0, "welford_mean_var_kernel",
      using accscalar_t = at::acc_type<scalar_t_0, true>;
      welford_kernel<scalar_t_0, accscalar_t, accscalar_t><<<grid, block, 0, stream>>>(
mcarilli's avatar
mcarilli committed
967
968
969
          input.DATA_PTR<scalar_t_0>(),
          out_mean.DATA_PTR<accscalar_t>(),
          out_var_biased.DATA_PTR<accscalar_t>(),
970
971
972
          batch_size,
          feature_size,
          space_size);
973
    );
974
  }
jjsjann123's avatar
jjsjann123 committed
975

Jie's avatar
Jie committed
976
  return {out_mean, out_var_biased};
jjsjann123's avatar
jjsjann123 committed
977
978
979
980
981
}

at::Tensor batchnorm_forward_CUDA(
    const at::Tensor input,
    const at::Tensor mean,
Jie's avatar
Jie committed
982
    const at::Tensor inv_std,
983
984
    const at::optional<at::Tensor> weight,
    const at::optional<at::Tensor> shift) {
jjsjann123's avatar
jjsjann123 committed
985
986
987
988
989
990
  const auto batch_size = input.size(0);
  const auto feature_size = input.size(1);
  at::Tensor out = at::empty_like(input);

  auto space_size = get_tensor_spatial_size(input);

Jeff Daily's avatar
Jeff Daily committed
991
  int block_x = max(WARP_SIZE, min(MAX_BLOCK_SIZE, h_last_pow2(space_size)/4));
Jie's avatar
Jie committed
992
993
994
995
996
  int block_y = max(1, min(MAX_BLOCK_SIZE/block_x, h_last_pow2(batch_size)/4));
  const dim3 block(block_x, block_y);
  int grid_z = max(1, min(65535, h_last_pow2(space_size)/4/block_x));
  int batch_group_size = max(1, min(65535, h_last_pow2(batch_size)/block_y));
  const dim3 grid(feature_size, batch_group_size, grid_z);
jjsjann123's avatar
jjsjann123 committed
997
998
  auto stream = at::cuda::getCurrentCUDAStream();

999
  if (input.scalar_type() == at::ScalarType::Half
1000
      && weight.has_value() &&
1001
      weight.value().scalar_type() == at::ScalarType::Float) {
1002
    using namespace at;
1003
1004
1005
    DISPATCH_FLOAT_AND_HALF(input.scalar_type(), 0, "batchnorm_forward",
      using accscalar_t = at::acc_type<scalar_t_0, true>;
      batchnorm_forward_kernel<scalar_t_0, accscalar_t, accscalar_t><<<grid, block, 0, stream>>>(
mcarilli's avatar
mcarilli committed
1006
1007
1008
1009
1010
1011
          input.DATA_PTR<scalar_t_0>(),
          mean.DATA_PTR<accscalar_t>(),
          inv_std.DATA_PTR<accscalar_t>(),
          weight.has_value() ? weight.value().DATA_PTR<accscalar_t>() : NULL,
          shift.has_value() ? shift.value().DATA_PTR<accscalar_t>() : NULL,
          out.DATA_PTR<scalar_t_0>(),
jjsjann123's avatar
jjsjann123 committed
1012
          space_size,
Jie's avatar
Jie committed
1013
          batch_size);
1014
    );
jjsjann123's avatar
jjsjann123 committed
1015
  } else {
1016
    if (weight.has_value()) {
1017
      TORCH_CHECK(input.scalar_type() == weight.value().scalar_type(),
1018
          "input.scalar_type() is not supported with weight.scalar_type()");
1019
    }
1020
    using namespace at;
1021
1022
1023
    DISPATCH_FLOAT_AND_HALF(input.scalar_type(), 0, "batchnorm_forward",
      using accscalar_t = at::acc_type<scalar_t_0, true>;
      batchnorm_forward_kernel<scalar_t_0, accscalar_t, scalar_t_0><<<grid, block, 0, stream>>>(
mcarilli's avatar
mcarilli committed
1024
1025
1026
1027
1028
1029
          input.DATA_PTR<scalar_t_0>(),
          mean.DATA_PTR<accscalar_t>(),
          inv_std.DATA_PTR<accscalar_t>(),
          weight.has_value() ? weight.value().DATA_PTR<scalar_t_0>() : NULL,
          shift.has_value() ? shift.value().DATA_PTR<scalar_t_0>() : NULL,
          out.DATA_PTR<scalar_t_0>(),
jjsjann123's avatar
jjsjann123 committed
1030
          space_size,
Jie's avatar
Jie committed
1031
          batch_size);
1032
    );
jjsjann123's avatar
jjsjann123 committed
1033
1034
1035
1036
1037
1038
1039
1040
  }
  return out;
}

std::vector<at::Tensor> reduce_bn_CUDA(
    const at::Tensor grad_output,
    const at::Tensor input,
    const at::Tensor mean,
Jie's avatar
Jie committed
1041
    const at::Tensor inv_std,
1042
    const at::optional<at::Tensor> weight)
jjsjann123's avatar
jjsjann123 committed
1043
1044
1045
1046
1047
1048
{
  const auto batch_size = input.size(0);
  const auto feature_size = input.size(1);

  auto scalar_type = promote_scalartype(input);

jjsjann123's avatar
jjsjann123 committed
1049
1050
  at::Tensor sum_dy = at::empty({feature_size}, mean.options());
  at::Tensor sum_dy_xmu = at::empty({feature_size}, mean.options());
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060

  at::Tensor grad_weight;
  at::Tensor grad_bias;
  if (weight.has_value()) {
    grad_weight = at::empty({feature_size}, weight.value().options());
    grad_bias = at::empty({feature_size}, weight.value().options());
  } else {
    grad_weight = at::empty({0}, mean.options());
    grad_bias = at::empty({0}, mean.options());
  }
jjsjann123's avatar
jjsjann123 committed
1061
1062
1063

  auto space_size = get_tensor_spatial_size(input);

Jeff Daily's avatar
Jeff Daily committed
1064
  int block_y = min(h_last_pow2(batch_size), int(MAX_BLOCK_SIZE/ WARP_SIZE));
Jie's avatar
Jie committed
1065
  int block_x = max(1, min(MAX_BLOCK_SIZE/ block_y, h_last_pow2(space_size)));
jjsjann123's avatar
jjsjann123 committed
1066
1067
1068
1069
  const dim3 block(block_x, block_y);
  const dim3 grid(feature_size);
  auto stream = at::cuda::getCurrentCUDAStream();

1070
  if (input.scalar_type() == at::ScalarType::Half
1071
      && weight.has_value() &&
1072
      weight.value().scalar_type() == at::ScalarType::Float) {
1073
    using namespace at;
1074
1075
1076
    DISPATCH_FLOAT_AND_HALF(input.scalar_type(), 0, "batchnorm_backward_reduce",
      using accscalar_t = at::acc_type<scalar_t_0, true>;
      reduce_bn_kernel<scalar_t_0, accscalar_t, accscalar_t><<<grid, block, 0, stream>>>(
mcarilli's avatar
mcarilli committed
1077
1078
1079
1080
          input.DATA_PTR<scalar_t_0>(),
          grad_output.DATA_PTR<scalar_t_0>(),
          mean.DATA_PTR<accscalar_t>(),
          inv_std.DATA_PTR<accscalar_t>(),
jjsjann123's avatar
jjsjann123 committed
1081
1082
          sum_dy.DATA_PTR<accscalar_t>(),
          sum_dy_xmu.DATA_PTR<accscalar_t>(),
mcarilli's avatar
mcarilli committed
1083
1084
          weight.has_value() ? grad_weight.DATA_PTR<accscalar_t>() : NULL,
          weight.has_value() ? grad_bias.DATA_PTR<accscalar_t>() : NULL,
jjsjann123's avatar
jjsjann123 committed
1085
1086
          batch_size,
          feature_size,
Jie's avatar
Jie committed
1087
          space_size);
1088
    );
jjsjann123's avatar
jjsjann123 committed
1089
  } else {
1090
    if (weight.has_value()) {
1091
        TORCH_CHECK(input.scalar_type() == weight.value().scalar_type(),
1092
            "input.scalar_type() is not supported with weight.scalar_type()");
1093
    }
1094
    using namespace at;
1095
1096
1097
    DISPATCH_FLOAT_AND_HALF(input.scalar_type(), 0, "batchnorm_backward_reduce",
      using accscalar_t = at::acc_type<scalar_t_0, true>;
      reduce_bn_kernel<scalar_t_0, accscalar_t, scalar_t_0><<<grid, block, 0, stream>>>(
mcarilli's avatar
mcarilli committed
1098
1099
1100
1101
          input.DATA_PTR<scalar_t_0>(),
          grad_output.DATA_PTR<scalar_t_0>(),
          mean.DATA_PTR<accscalar_t>(),
          inv_std.DATA_PTR<accscalar_t>(),
jjsjann123's avatar
jjsjann123 committed
1102
1103
          sum_dy.DATA_PTR<accscalar_t>(),
          sum_dy_xmu.DATA_PTR<accscalar_t>(),
mcarilli's avatar
mcarilli committed
1104
1105
          weight.has_value() ? grad_weight.DATA_PTR<scalar_t_0>() : NULL,
          weight.has_value() ? grad_bias.DATA_PTR<scalar_t_0>() : NULL,
jjsjann123's avatar
jjsjann123 committed
1106
1107
          batch_size,
          feature_size,
Jie's avatar
Jie committed
1108
          space_size);
1109
    );
jjsjann123's avatar
jjsjann123 committed
1110
  }
Jie's avatar
Jie committed
1111

jjsjann123's avatar
jjsjann123 committed
1112
  return {sum_dy, sum_dy_xmu, grad_weight, grad_bias};
jjsjann123's avatar
jjsjann123 committed
1113
1114
1115
1116
1117
1118
}

at::Tensor batchnorm_backward_CUDA(
    const at::Tensor grad_output,
    const at::Tensor input,
    const at::Tensor mean,
Jie's avatar
Jie committed
1119
    const at::Tensor inv_std,
1120
    const at::optional<at::Tensor> weight,
jjsjann123's avatar
jjsjann123 committed
1121
1122
1123
    const at::Tensor sum_dy,
    const at::Tensor sum_dy_xmu,
    const at::Tensor count) {
jjsjann123's avatar
jjsjann123 committed
1124
1125
1126
1127
1128
1129
1130
  const auto batch_size = input.size(0);
  const auto feature_size = input.size(1);

  at::Tensor grad_input = at::empty_like(input);

  auto space_size = get_tensor_spatial_size(input);

Jeff Daily's avatar
Jeff Daily committed
1131
  int block_x = max(WARP_SIZE, min(MAX_BLOCK_SIZE, h_last_pow2(space_size)/4));
Jie's avatar
Jie committed
1132
1133
1134
1135
1136
1137
  int block_y = max(1, min(MAX_BLOCK_SIZE/block_x, h_last_pow2(batch_size)/4));
  const dim3 block(block_x, block_y);
  int grid_z = max(1, min(65535, h_last_pow2(space_size)/4/block_x));
  int batch_group_size = max(1, min(65535, h_last_pow2(batch_size)/block_y));
  const dim3 grid(feature_size, batch_group_size, grid_z);

jjsjann123's avatar
jjsjann123 committed
1138
1139
  auto stream = at::cuda::getCurrentCUDAStream();

1140
  if (input.scalar_type() == at::ScalarType::Half
1141
      && weight.has_value() &&
1142
      weight.value().scalar_type() == at::ScalarType::Float) {
1143
    using namespace at;
1144
1145
1146
    DISPATCH_FLOAT_AND_HALF(input.scalar_type(), 0, "batchnorm_backward",
      using accscalar_t = at::acc_type<scalar_t_0, true>;
      batchnorm_backward_kernel<scalar_t_0, accscalar_t, accscalar_t><<<grid, block, 0, stream>>>(
mcarilli's avatar
mcarilli committed
1147
1148
1149
1150
1151
          grad_output.DATA_PTR<scalar_t_0>(),
          input.DATA_PTR<scalar_t_0>(),
          mean.DATA_PTR<accscalar_t>(),
          inv_std.DATA_PTR<accscalar_t>(),
          weight.has_value() ? weight.value().DATA_PTR<accscalar_t>() : NULL,
jjsjann123's avatar
jjsjann123 committed
1152
1153
1154
          sum_dy.DATA_PTR<accscalar_t>(),
          sum_dy_xmu.DATA_PTR<accscalar_t>(),
          count.DATA_PTR<int>(),
mcarilli's avatar
mcarilli committed
1155
          grad_input.DATA_PTR<scalar_t_0>(),
jjsjann123's avatar
jjsjann123 committed
1156
          count.numel(),
jjsjann123's avatar
jjsjann123 committed
1157
          space_size,
Jie's avatar
Jie committed
1158
          batch_size);
1159
    );
jjsjann123's avatar
jjsjann123 committed
1160
  } else {
1161
    if (weight.has_value()) {
1162
      TORCH_CHECK(input.scalar_type() == weight.value().scalar_type(),
1163
          "input.scalar_type() is not supported with weight.scalar_type()");
1164
    }
1165
    using namespace at;
1166
1167
1168
    DISPATCH_FLOAT_AND_HALF(input.scalar_type(), 0, "batchnorm_backward",
      using accscalar_t = at::acc_type<scalar_t_0, true>;
      batchnorm_backward_kernel<scalar_t_0, accscalar_t, scalar_t_0><<<grid, block, 0, stream>>>(
mcarilli's avatar
mcarilli committed
1169
1170
1171
1172
1173
          grad_output.DATA_PTR<scalar_t_0>(),
          input.DATA_PTR<scalar_t_0>(),
          mean.DATA_PTR<accscalar_t>(),
          inv_std.DATA_PTR<accscalar_t>(),
          weight.has_value() ? weight.value().DATA_PTR<scalar_t_0>() : NULL,
jjsjann123's avatar
jjsjann123 committed
1174
1175
1176
          sum_dy.DATA_PTR<accscalar_t>(),
          sum_dy_xmu.DATA_PTR<accscalar_t>(),
          count.DATA_PTR<int>(),
mcarilli's avatar
mcarilli committed
1177
          grad_input.DATA_PTR<scalar_t_0>(),
jjsjann123's avatar
jjsjann123 committed
1178
          count.numel(),
jjsjann123's avatar
jjsjann123 committed
1179
          space_size,
Jie's avatar
Jie committed
1180
          batch_size);
1181
    );
jjsjann123's avatar
jjsjann123 committed
1182
  }
Jie's avatar
Jie committed
1183

jjsjann123's avatar
jjsjann123 committed
1184
1185
1186
  return grad_input;
}

Jie's avatar
Jie committed
1187
1188
std::vector<at::Tensor> welford_parallel_CUDA(const at::Tensor mean_feature_nodes,
                                              const at::Tensor var_biased,
jjsjann123's avatar
jjsjann123 committed
1189
                                              const at::Tensor numel,
Jie's avatar
Jie committed
1190
1191
1192
                                              const float eps) {
  const auto world_size = mean_feature_nodes.size(0);
  const auto feature_size = mean_feature_nodes.size(1);
jjsjann123's avatar
jjsjann123 committed
1193
1194

  at::Tensor out_var = at::empty({feature_size}, var_biased.options());
Jie's avatar
Jie committed
1195
  at::Tensor inv_std = at::empty_like(out_var);
jjsjann123's avatar
jjsjann123 committed
1196
1197
  at::Tensor out_mean = at::empty_like(out_var);

1198
1199
1200
1201
  at::Tensor mean_feature_nodes_ = mean_feature_nodes.contiguous();
  at::Tensor var_biased_ = var_biased.contiguous();
  at::Tensor numel_ = numel.contiguous();

jjsjann123's avatar
jjsjann123 committed
1202
  // TODO(jie): tile this for memory coalescing!
Jie's avatar
Jie committed
1203
1204
1205
  const int block = std::min(h_last_pow2(feature_size), MAX_BLOCK_SIZE);
  const int grid = std::max<int>(1, feature_size / block);

jjsjann123's avatar
jjsjann123 committed
1206
1207
  auto stream = at::cuda::getCurrentCUDAStream();

1208
1209
  {
    using namespace at;
1210
1211
    DISPATCH_FLOAT_AND_HALF(mean_feature_nodes.scalar_type(), 0, "welford_parallel_kernel",
      welford_kernel_parallel<scalar_t_0><<<grid, block, 0, stream>>>(
1212
1213
1214
          mean_feature_nodes_.DATA_PTR<scalar_t_0>(),
          var_biased_.DATA_PTR<scalar_t_0>(),
          numel_.DATA_PTR<int>(),
mcarilli's avatar
mcarilli committed
1215
1216
1217
          out_mean.DATA_PTR<scalar_t_0>(),
          out_var.DATA_PTR<scalar_t_0>(),
          inv_std.DATA_PTR<scalar_t_0>(),
1218
1219
          world_size,
          feature_size,
jjsjann123's avatar
jjsjann123 committed
1220
          eps);
1221
    );
1222
  }
jjsjann123's avatar
jjsjann123 committed
1223

Jie's avatar
Jie committed
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
  return {out_mean, out_var, inv_std};
}

std::vector<at::Tensor> welford_mean_var_c_last_CUDA(const at::Tensor input) {
  const auto stride = input.size(input.ndimension()-1);
  const auto reduction_size = input.numel() / stride;

  auto scalar_type = promote_scalartype(input);
  auto option = input.options().dtype(scalar_type);

  at::Tensor out_var_biased = at::empty({stride}, option);
  at::Tensor out_mean = at::empty({stride}, option);

  dim3 block;
  dim3 grid;
  flexible_launch_configs(reduction_size, stride, block, grid, true);

  at::Tensor staging_data;
  at::Tensor semaphores;
  if (grid.y > 1) {
    staging_data = at::empty({4*stride*grid.y}, option);
1245
    semaphores = at::zeros({grid.x}, input.options().dtype(at::kInt));
Jie's avatar
Jie committed
1246
1247
1248
1249
  }

  auto stream = at::cuda::getCurrentCUDAStream();

1250
1251
  {
    using namespace at;
1252
1253
    DISPATCH_FLOAT_AND_HALF(input.scalar_type(), 0, "welford_mean_var_c_last",
      using accscalar_t = at::acc_type<scalar_t_0, true>;
mcarilli's avatar
mcarilli committed
1254
1255
      accscalar_t* staging_data_ptr = grid.y > 1 ? staging_data.DATA_PTR<accscalar_t>() : nullptr;
      int* semaphores_ptr = grid.y > 1 ? semaphores.DATA_PTR<int>() : nullptr;
1256
      welford_kernel_c_last<scalar_t_0, accscalar_t, accscalar_t, ELEMENTS_PER_ITER>
1257
          <<<grid, block, 0, stream>>>(
mcarilli's avatar
mcarilli committed
1258
1259
1260
          input.DATA_PTR<scalar_t_0>(),
          out_mean.DATA_PTR<accscalar_t>(),
          out_var_biased.DATA_PTR<accscalar_t>(),
1261
1262
1263
1264
          staging_data_ptr,
          semaphores_ptr,
          reduction_size,
          stride);
1265
    );
1266
  }
Jie's avatar
Jie committed
1267
1268
1269
1270
1271
1272

  return {out_mean, out_var_biased};
}

at::Tensor batchnorm_forward_c_last_CUDA(
    const at::Tensor input,
jjsjann123's avatar
jjsjann123 committed
1273
    const at::optional<at::Tensor> z,
Jie's avatar
Jie committed
1274
1275
    const at::Tensor mean,
    const at::Tensor inv_std,
1276
    const at::optional<at::Tensor> weight,
jjsjann123's avatar
jjsjann123 committed
1277
1278
    const at::optional<at::Tensor> shift,
    const bool fuse_relu) {
Jie's avatar
Jie committed
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
  const auto stride = input.size(input.ndimension()-1);
  const auto reduction_size = input.numel() / stride;

  at::Tensor out = at::empty_like(input);

  dim3 block;
  dim3 grid;
  flexible_launch_configs(reduction_size, stride, block, grid);

  auto stream = at::cuda::getCurrentCUDAStream();

1290
1291
  if (input.scalar_type() == at::ScalarType::Half
      && weight.has_value() && weight.value().scalar_type() == at::ScalarType::Float) {
1292
    using namespace at;
1293
1294
1295
    DISPATCH_FLOAT_AND_HALF(input.scalar_type(), 0, "batchnorm_forward",
      using accscalar_t = at::acc_type<scalar_t_0, true>;
      batchnorm_forward_c_last_kernel<scalar_t_0, accscalar_t, accscalar_t, ELEMENTS_PER_ITER>
Jie's avatar
Jie committed
1296
          <<<grid, block, 0, stream>>>(
mcarilli's avatar
mcarilli committed
1297
1298
1299
1300
1301
1302
1303
          input.DATA_PTR<scalar_t_0>(),
          z.has_value() ? z.value().DATA_PTR<scalar_t_0>() : NULL,
          mean.DATA_PTR<accscalar_t>(),
          inv_std.DATA_PTR<accscalar_t>(),
          weight.has_value() ? weight.value().DATA_PTR<accscalar_t>() : NULL,
          shift.has_value() ? shift.value().DATA_PTR<accscalar_t>(): NULL,
          out.DATA_PTR<scalar_t_0>(),
Jie's avatar
Jie committed
1304
          reduction_size,
jjsjann123's avatar
jjsjann123 committed
1305
1306
          stride,
          fuse_relu);
1307
    );
Jie's avatar
Jie committed
1308
  } else {
1309
    if (weight.has_value()) {
1310
      TORCH_CHECK(input.scalar_type() == weight.value().scalar_type(),
1311
          "input.scalar_type() is not supported with weight.scalar_type()");
1312
    }
1313
    using namespace at;
1314
1315
1316
    DISPATCH_FLOAT_AND_HALF(input.scalar_type(), 0, "batchnorm_forward",
      using accscalar_t = at::acc_type<scalar_t_0, true>;
      batchnorm_forward_c_last_kernel<scalar_t_0, accscalar_t, scalar_t_0, ELEMENTS_PER_ITER>
Jie's avatar
Jie committed
1317
          <<<grid, block, 0, stream>>>(
mcarilli's avatar
mcarilli committed
1318
1319
1320
1321
1322
1323
1324
          input.DATA_PTR<scalar_t_0>(),
          z.has_value() ? z.value().DATA_PTR<scalar_t_0>() : NULL,
          mean.DATA_PTR<accscalar_t>(),
          inv_std.DATA_PTR<accscalar_t>(),
          weight.has_value() ? weight.value().DATA_PTR<scalar_t_0>() : NULL,
          shift.has_value() ? shift.value().DATA_PTR<scalar_t_0>(): NULL,
          out.DATA_PTR<scalar_t_0>(),
Jie's avatar
Jie committed
1325
          reduction_size,
jjsjann123's avatar
jjsjann123 committed
1326
1327
          stride,
          fuse_relu);
1328
    );
Jie's avatar
Jie committed
1329
1330
1331
1332
1333
1334
1335
1336
1337
  }
  return out;
}

std::vector<at::Tensor> reduce_bn_c_last_CUDA(
    const at::Tensor grad_output,
    const at::Tensor input,
    const at::Tensor mean,
    const at::Tensor inv_std,
1338
    const at::optional<at::Tensor> weight) {
Jie's avatar
Jie committed
1339
1340
1341
  const auto stride = input.size(input.ndimension()-1);
  const auto reduction_size = input.numel() / stride;

jjsjann123's avatar
jjsjann123 committed
1342
1343
  at::Tensor sumn_dy = at::empty({stride}, mean.options());
  at::Tensor sum_dy_xmu = at::empty({stride}, mean.options());
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354

  at::Tensor grad_weight;
  at::Tensor grad_bias;
  if (weight.has_value()) {
    grad_weight = at::empty({stride}, weight.value().options());
    grad_bias = at::empty({stride}, weight.value().options());
  } else {
    // because I cannot return an uninitialized at::Tensor
    grad_weight = at::empty({0}, mean.options());
    grad_bias = at::empty({0}, mean.options());
  }
Jie's avatar
Jie committed
1355
1356
1357
1358
1359
1360
1361
1362
1363

  dim3 block;
  dim3 grid;
  flexible_launch_configs(reduction_size, stride, block, grid, true);

  at::Tensor staging_data;
  at::Tensor semaphores;
  if (grid.y > 1) {
    staging_data = at::empty({2*stride*grid.y}, mean.options());
1364
    semaphores = at::zeros({grid.x}, input.options().dtype(at::kInt));
Jie's avatar
Jie committed
1365
1366
1367
  }
  auto stream = at::cuda::getCurrentCUDAStream();

1368
  if (input.scalar_type() == at::ScalarType::Half
1369
      && weight.has_value()
1370
      && weight.value().scalar_type() == at::ScalarType::Float) {
1371
    using namespace at;
1372
1373
    DISPATCH_FLOAT_AND_HALF(input.scalar_type(), 0, "batchnorm_backward_reduce",
      using accscalar_t = at::acc_type<scalar_t_0, true>;
mcarilli's avatar
mcarilli committed
1374
1375
      accscalar_t* staging_data_ptr = grid.y > 1 ? staging_data.DATA_PTR<accscalar_t>() : nullptr;
      int* semaphores_ptr = grid.y > 1 ? semaphores.DATA_PTR<int>() : nullptr;
1376
      reduce_bn_c_last_kernel<scalar_t_0, accscalar_t, accscalar_t, ELEMENTS_PER_ITER>
Jie's avatar
Jie committed
1377
          <<<grid, block, 0, stream>>>(
mcarilli's avatar
mcarilli committed
1378
1379
1380
1381
          input.DATA_PTR<scalar_t_0>(),
          grad_output.DATA_PTR<scalar_t_0>(),
          mean.DATA_PTR<accscalar_t>(),
          inv_std.DATA_PTR<accscalar_t>(),
jjsjann123's avatar
jjsjann123 committed
1382
1383
          sumn_dy.DATA_PTR<accscalar_t>(),
          sum_dy_xmu.DATA_PTR<accscalar_t>(),
mcarilli's avatar
mcarilli committed
1384
1385
          weight.has_value() ? grad_weight.DATA_PTR<accscalar_t>() : NULL,
          weight.has_value() ?grad_bias.DATA_PTR<accscalar_t>() : NULL,
Jie's avatar
Jie committed
1386
1387
1388
1389
          staging_data_ptr,
          semaphores_ptr,
          reduction_size,
          stride);
1390
    );
Jie's avatar
Jie committed
1391
  } else {
1392
    if (weight.has_value()) {
1393
      TORCH_CHECK(input.scalar_type() == weight.value().scalar_type(),
1394
          "input.scalar_type() is not supported with weight.scalar_type()");
1395
    }
1396
    using namespace at;
1397
1398
    DISPATCH_FLOAT_AND_HALF(input.scalar_type(), 0, "batchnorm_backward_reduce",
      using accscalar_t = at::acc_type<scalar_t_0, true>;
mcarilli's avatar
mcarilli committed
1399
1400
      accscalar_t* staging_data_ptr = grid.y > 1 ? staging_data.DATA_PTR<accscalar_t>() : nullptr;
      int* semaphores_ptr = grid.y > 1 ? semaphores.DATA_PTR<int>() : nullptr;
1401
      reduce_bn_c_last_kernel<scalar_t_0, accscalar_t, scalar_t_0, ELEMENTS_PER_ITER>
Jie's avatar
Jie committed
1402
          <<<grid, block, 0, stream>>>(
mcarilli's avatar
mcarilli committed
1403
1404
1405
1406
          input.DATA_PTR<scalar_t_0>(),
          grad_output.DATA_PTR<scalar_t_0>(),
          mean.DATA_PTR<accscalar_t>(),
          inv_std.DATA_PTR<accscalar_t>(),
jjsjann123's avatar
jjsjann123 committed
1407
1408
          sumn_dy.DATA_PTR<accscalar_t>(),
          sum_dy_xmu.DATA_PTR<accscalar_t>(),
mcarilli's avatar
mcarilli committed
1409
1410
          weight.has_value() ? grad_weight.DATA_PTR<scalar_t_0>() : NULL,
          weight.has_value() ?grad_bias.DATA_PTR<scalar_t_0>() : NULL,
Jie's avatar
Jie committed
1411
1412
1413
1414
          staging_data_ptr,
          semaphores_ptr,
          reduction_size,
          stride);
1415
    );
Jie's avatar
Jie committed
1416
1417
  }

jjsjann123's avatar
jjsjann123 committed
1418
  return {sumn_dy, sum_dy_xmu, grad_weight, grad_bias};
Jie's avatar
Jie committed
1419
1420
1421
1422
1423
1424
1425
}

at::Tensor batchnorm_backward_c_last_CUDA(
    const at::Tensor grad_output,
    const at::Tensor input,
    const at::Tensor mean,
    const at::Tensor inv_std,
1426
    const at::optional<at::Tensor> weight,
jjsjann123's avatar
jjsjann123 committed
1427
1428
1429
    const at::Tensor sum_dy,
    const at::Tensor sum_dy_xmu,
    const at::Tensor count) {
Jie's avatar
Jie committed
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
  const auto stride = input.size(input.ndimension()-1);
  const auto reduction_size = input.numel() / stride;

  at::Tensor grad_input = at::empty_like(input);

  dim3 block;
  dim3 grid;
  flexible_launch_configs(reduction_size, stride, block, grid);

  auto stream = at::cuda::getCurrentCUDAStream();

1441
1442
  if (input.scalar_type() == at::ScalarType::Half
      && weight.has_value() && weight.value().scalar_type() == at::ScalarType::Float) {
1443
    using namespace at;
1444
1445
1446
    DISPATCH_FLOAT_AND_HALF(input.scalar_type(), 0, "batchnorm_forward",
      using accscalar_t = at::acc_type<scalar_t_0, true>;
      batchnorm_backward_c_last_kernel<scalar_t_0, accscalar_t, accscalar_t, ELEMENTS_PER_ITER>
Jie's avatar
Jie committed
1447
          <<<grid, block, 0, stream>>>(
mcarilli's avatar
mcarilli committed
1448
1449
1450
1451
1452
          grad_output.DATA_PTR<scalar_t_0>(),
          input.DATA_PTR<scalar_t_0>(),
          mean.DATA_PTR<accscalar_t>(),
          inv_std.DATA_PTR<accscalar_t>(),
          weight.has_value() ? weight.value().DATA_PTR<accscalar_t>() : NULL,
jjsjann123's avatar
jjsjann123 committed
1453
1454
1455
          sum_dy.DATA_PTR<accscalar_t>(),
          sum_dy_xmu.DATA_PTR<accscalar_t>(),
          count.DATA_PTR<int>(),
mcarilli's avatar
mcarilli committed
1456
          grad_input.DATA_PTR<scalar_t_0>(),
jjsjann123's avatar
jjsjann123 committed
1457
          count.numel(),
Jie's avatar
Jie committed
1458
1459
          reduction_size,
          stride);
1460
    );
Jie's avatar
Jie committed
1461
  } else {
1462
    if (weight.has_value()) {
1463
      TORCH_CHECK(input.scalar_type() == weight.value().scalar_type(),
1464
          "input.scalar_type() is not supported with weight.scalar_type()");
1465
    }
1466
    using namespace at;
1467
1468
1469
    DISPATCH_FLOAT_AND_HALF(input.scalar_type(), 0, "batchnorm_forward",
      using accscalar_t = at::acc_type<scalar_t_0, true>;
      batchnorm_backward_c_last_kernel<scalar_t_0, accscalar_t, scalar_t_0, ELEMENTS_PER_ITER>
Jie's avatar
Jie committed
1470
          <<<grid, block, 0, stream>>>(
mcarilli's avatar
mcarilli committed
1471
1472
1473
1474
1475
          grad_output.DATA_PTR<scalar_t_0>(),
          input.DATA_PTR<scalar_t_0>(),
          mean.DATA_PTR<accscalar_t>(),
          inv_std.DATA_PTR<accscalar_t>(),
          weight.has_value() ? weight.value().DATA_PTR<scalar_t_0>() : NULL,
jjsjann123's avatar
jjsjann123 committed
1476
1477
1478
          sum_dy.DATA_PTR<accscalar_t>(),
          sum_dy_xmu.DATA_PTR<accscalar_t>(),
          count.DATA_PTR<int>(),
mcarilli's avatar
mcarilli committed
1479
          grad_input.DATA_PTR<scalar_t_0>(),
jjsjann123's avatar
jjsjann123 committed
1480
          count.numel(),
Jie's avatar
Jie committed
1481
1482
          reduction_size,
          stride);
1483
    );
Jie's avatar
Jie committed
1484
1485
1486
  }
 
  return grad_input;
jjsjann123's avatar
jjsjann123 committed
1487
}
jjsjann123's avatar
jjsjann123 committed
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515

at::Tensor relu_backward_c_last_CUDA(
    const at::Tensor grad_output,
    const at::Tensor input,
    const at::optional<at::Tensor> z,
    const at::Tensor mean,
    const at::Tensor inv_std,
    const at::optional<at::Tensor> weight,
    const at::optional<at::Tensor> shift) {

  const auto stride = input.size(input.ndimension()-1);
  const auto reduction_size = input.numel() / stride;

  at::Tensor out = at::empty_like(input);

  dim3 block;
  dim3 grid;
  flexible_launch_configs(reduction_size, stride, block, grid);

  auto stream = at::cuda::getCurrentCUDAStream();

  if (input.scalar_type() == at::ScalarType::Half
      && weight.has_value() && weight.value().scalar_type() == at::ScalarType::Float) {
    using namespace at;
    DISPATCH_FLOAT_AND_HALF(input.scalar_type(), 0, "batchnorm_forward",
      using accscalar_t = at::acc_type<scalar_t_0, true>;
      relu_backward_c_last_kernel<scalar_t_0, accscalar_t, accscalar_t, ELEMENTS_PER_ITER>
          <<<grid, block, 0, stream>>>(
mcarilli's avatar
mcarilli committed
1516
1517
1518
1519
1520
1521
1522
1523
          grad_output.DATA_PTR<scalar_t_0>(),
          input.DATA_PTR<scalar_t_0>(),
          z.has_value() ? z.value().DATA_PTR<scalar_t_0>() : NULL,
          mean.DATA_PTR<accscalar_t>(),
          inv_std.DATA_PTR<accscalar_t>(),
          weight.has_value() ? weight.value().DATA_PTR<accscalar_t>() : NULL,
          shift.has_value() ? shift.value().DATA_PTR<accscalar_t>(): NULL,
          out.DATA_PTR<scalar_t_0>(),
jjsjann123's avatar
jjsjann123 committed
1524
1525
1526
1527
1528
          reduction_size,
          stride);
    );
  } else {
    if (weight.has_value()) {
1529
      TORCH_CHECK(input.scalar_type() == weight.value().scalar_type(),
jjsjann123's avatar
jjsjann123 committed
1530
1531
1532
1533
1534
1535
1536
          "input.scalar_type() is not supported with weight.scalar_type()");
    }
    using namespace at;
    DISPATCH_FLOAT_AND_HALF(input.scalar_type(), 0, "batchnorm_forward",
      using accscalar_t = at::acc_type<scalar_t_0, true>;
      relu_backward_c_last_kernel<scalar_t_0, accscalar_t, scalar_t_0, ELEMENTS_PER_ITER>
          <<<grid, block, 0, stream>>>(
mcarilli's avatar
mcarilli committed
1537
1538
1539
1540
1541
1542
1543
1544
          grad_output.DATA_PTR<scalar_t_0>(),
          input.DATA_PTR<scalar_t_0>(),
          z.has_value() ? z.value().DATA_PTR<scalar_t_0>() : NULL,
          mean.DATA_PTR<accscalar_t>(),
          inv_std.DATA_PTR<accscalar_t>(),
          weight.has_value() ? weight.value().DATA_PTR<scalar_t_0>() : NULL,
          shift.has_value() ? shift.value().DATA_PTR<scalar_t_0>(): NULL,
          out.DATA_PTR<scalar_t_0>(),
jjsjann123's avatar
jjsjann123 committed
1545
1546
1547
1548
1549
1550
          reduction_size,
          stride);
    );
  }
  return out;
}